Triple

T16776872
Position Surface form Disambiguated ID Type / Status
Subject Payaguá people E407746 entity
Predicate historicalRegion P915 FINISHED
Object Gran Chaco E136889 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Gran Chaco | Statement: [Payaguá people, historicalRegion, Gran Chaco]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gran Chaco
Context triple: [Payaguá people, historicalRegion, Gran Chaco]
  • A. Gran Chaco chosen
    The Gran Chaco is a vast, sparsely populated lowland plain in central South America, known for its hot, semi-arid climate and dry forests spanning parts of Argentina, Paraguay, Bolivia, and Brazil.
  • B. Misiones rainforest
    The Misiones rainforest is a subtropical forest in northeastern Argentina renowned for its rich biodiversity, red-soil landscapes, and iconic Iguazú Falls.
  • C. Pampas
    The Pampas is a vast fertile lowland plain in South America, primarily in Argentina, known for its grasslands, agriculture, and cattle ranching.
  • D. Pampa
    Pampa is a jet trainer aircraft used by the Argentine Air Force, known for its role in pilot training and light attack missions.
  • E. Pampa
    Pampa was a pioneering 10th-century Kannada poet, celebrated as one of the “three gems” of classical Kannada literature and best known for his epic works like the Adipurana and Vikramarjuna Vijaya.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d8839270588190886720d9519bbf8f completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3b03a646c8190b3944c9f0c25af27 completed April 18, 2026, 4:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00bb0911488190a65c1dc536b6ea3e completed May 10, 2026, 5:06 p.m.
Created at: April 10, 2026, 5:22 a.m.